Articles by OSM

Not so soft softmax

April 2, 2021 | OSM

Our last post examined the correspondence between a logistic regression and a simple neural network using a sigmoid activation function. The downside with such models is that they only produce binary outcomes. While we argued (not very forcefully) that if investing is about assessing the probability of achieving an attractive ...
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Activate sigmoid!

March 12, 2021 | OSM

In our last post, we introduced neural networks and formulated some of the questions we want to explore over this series. We explained the underlying architecture, the basics of the algorithm, and showed how a simple neural network could approximate the results and parameters of a linear regression. In this ...
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Nothing but (neural) net

February 26, 2021 | OSM

We start a new series on neural networks and deep learning. Neural networks and their use in finance are not new. But are still only a fraction of the research output. A recent Google scholar search found only 6% of the articles on stock price price forecasting discussed neural networks.1 Artificial ...
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Risk-constrained optimization

February 5, 2021 | OSM

Our last post parsed portfolio optimization outputs and examined some of the nuances around the efficient frontier. We noted that when you start building portfolios with a large number of assets, brute force simulation can miss the optimal weighting scheme for a given return or risk profile. While optimization finds ...
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Parsing portfolio optimization

January 31, 2021 | OSM

Our last few posts on risk factor models haven’t discussed how we might use such a model in the portfolio optimization process. Indeed, although we’ve touched on mean-variance optimization, efficient frontiers, and maximum Sharpe ratios in this portfolio series, we haven’t discussed portfolio optimization and its outputs ...
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More factors, more variance…explained

January 15, 2021 | OSM

Risk factor models are at the core of quantitative investing. We’ve been exploring their application within our portfolio series to see if we could create such a model to quantify risk better than using a simplistic volatility measure. That is, given our four portfolios (Satisfactory, Naive, Max Sharpe, and ...
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Macro variance

December 31, 2020 | OSM

In our last post, we looked at using a risk factor model to identify potential sources of variance for our 30,000 portfolio simulations. We introduced the process with a view ultimately to construct a model that could help to quantify, and thus mitigate, sources of risk beyond a simplistic volatility measure. ...
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December 28, 2020 | OSM

Python-bloggers aggregates blogs focused on using Python’s data analysis super-power for data science, machine learning, and statistics. Brought to you by the same folks that publish the hugely popular R-bloggers, it is well worth a read. Check it... [...Read more...]


December 28, 2020 | OSM

Quantocracy is a great resource for all things related to quantitative and empirical investing. We learn something every time we visit. Expand your knowledge here!
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December 28, 2020 | OSM

R-bloggers is a great resource. We visit the website almost every day. Shouldn’t you? Have a look [...Read more...]
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